Development of an Interpretable Machine Learning Model to Predict Pregnancy Outcomes Following Cervical Cerclage

The RF model provided a clinically useful and interpretable framework for predicting outcomes after cerclage, emphasizing inflammatory status, maternal characteristics, and cerclage indication as key determinants of preterm birth. An online prediction tool was developed to facilitate individualized risk assessment. Despite the retrospective, single-center design and lack of external validation, these findings support the integration of ML into clinical decision-making, and warrant multicenter prospective validation.